Catalin Mates
Data Scientist @Sauce Labs
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WORK HISTORY
Data Scientist @Sauce Labs
Vancouver, BC, CA
Leveraging machine learning to understand user behaviour, I created customer profiles via clustering techniques. This work drove targeted engagement strategies and helped 30% of our users move from manual to automated testing - Used machine learning and simulation techniques to forecast Virtual Device Cloud machine demand, achieving optimized utilization and paving the way for significant cost reduction, with projected monthly savings nearing $100K - Utilized regressor to reprice products impacting nearly $90M in business. In collaboration with finance and product teams, I built dynamic data apps using Streamlit, which provided clarity on data usage, assumptions, insights, selected models, and impact. This offered ongoing analytical insights and facilitated a better understanding of the data story - I engage in a weekly exploration of research papers on advanced artificial intelligence techniques, seeking potential applications within the Sauce Labs product range- Other data science work includes churn/retention analysis, test orchestration, pattern analysis, overages analysis
EDUCATION
University of Toronto
Actuarial Science and Statistics, Actuarial, Statistics, Mathematics, Economics
ABOUT CATALIN MATES
As a Data Scientist, I leverage machine learning and data storytelling to transform business decisions in product and finance domains. I have over 5 years of hands-on experience in data science and machine learning, using Python, R, SQL, and AWS/GCP tools. I collaborate with cross-functional teams to build dynamic data apps, develop machine learning proof of concepts. We\'re using machine learning solutions across the board, from risk modeling to product development, marketing, and sales strategies. Some of my achievements include creating customer profiles via clustering techniques, forecasting Virtual Device Cloud machine demand, and repricing products impacting nearly $90M in business. Recently, I\'ve ventured into Large Language Models (LLMs), focusing on retrieval augmentation generation. I use LLM agents to interact with diverse data, expanding document search capabilities and optimizing data queries. My passion is to bring machine learning theory to practice and deliver actionable insights that drive value and growth. Technical Skills Programming: Python, SQL, Looker, R, SAS, Git Databases and Datastores: Snowflake, AWS: S3, Redshift, RDS, GCP: VertexAI Machine Learning: Scikit-learn, NumPy, Pandas, Streamlit, TensorFlow, Matplotlib, GGplot
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